Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neuroscience-Inspired Hierarchical GNN for Grasping Attempt Classification.

IEEE journal of biomedical and health informatics·2026
Same author

Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.

NeuroSci·2026
Same author

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Decoupled Hierarchical Distillation for Multimodal Emotion Recognition.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

EEG-to-gait decoding via phase-aware representation learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Decoding Covert Speech From EEG by Functional Areas Spatio-Temporal Transformer.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: May 11, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface.

Mahnaz Arvaneh, Cuntai Guan, Kai Keng Ang

    Neural Computation
    |May 14, 2013
    PubMed
    Summary

    This study introduces EEG data space adaptation (EEG-DSA) to improve brain-computer interface (BCI) performance by reducing data variability between sessions. EEG-DSA significantly enhances classification accuracy for motor imagery tasks.

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Brain-computer interfaces (BCIs) face performance degradation due to intersession nonstationarity in electroencephalography (EEG) data.

    More Related Videos

    STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
    05:36

    STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

    Published on: March 10, 2026

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    Related Experiment Videos

    Last Updated: May 11, 2026

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
    06:34

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

    Published on: July 7, 2023

    STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
    05:36

    STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

    Published on: March 10, 2026

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

  • This variability hinders the reliability and accuracy of BCI systems across different usage sessions.